'''simple docstring'''




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class        a__ :






     def __init__(					self						:						List[str]    ,							a						:						int=2    ,							a						:						Union[str, Any]=3    ,							a						:						Tuple=64    ,							a						:						Optional[Any]=None       ):





         """simple docstring"""



         __lowerCamelCase            =    np.random.default_rng(a       )
         __lowerCamelCase            =    length
         __lowerCamelCase            =    rng.normal(size=(length,)       ).astype(np.floataa       )
         __lowerCamelCase            =    a * self.x + b + rng.normal(scale=0.1    ,							size=(length,)       ).astype(np.floataa       )






     def __len__(					self						:						str       ):





         """simple docstring"""



         return self.length






     def __getitem__(					self						:						int    ,							a						:						Union[str, Any]       ):





         """simple docstring"""



         return {"x": self.x[i], "y": self.y[i]}




class        a__ (  torch.nn.Module					):






     def __init__(					self						:						str    ,							a						:						str=0    ,							a						:						Optional[Any]=0    ,							a						:						List[Any]=False       ):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase            =    torch.nn.Parameter(torch.tensor([2, 3]       ).float()       )
         __lowerCamelCase            =    torch.nn.Parameter(torch.tensor([2, 3]       ).float()       )
         __lowerCamelCase            =    True






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]    ,							a						:						Optional[Any]=None       ):





         """simple docstring"""



         if self.first_batch:
             print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""       )
             __lowerCamelCase            =    False
         return x * self.a[0] + self.b[0]




class        a__ (  torch.nn.Module					):






     def __init__(					self						:						Any    ,							a						:						str=0    ,							a						:						Any=0    ,							a						:						Any=False       ):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase            =    torch.nn.Parameter(torch.tensor(a       ).float()       )
         __lowerCamelCase            =    torch.nn.Parameter(torch.tensor(a       ).float()       )
         __lowerCamelCase            =    True






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]    ,							a						:						Union[str, Any]=None       ):





         """simple docstring"""



         if self.first_batch:
             print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""       )
             __lowerCamelCase            =    False
         return x * self.a + self.b






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__ = 16							)      ->       Optional[int]:
    from datasets import load_dataset
    from transformers import AutoTokenizer

    __lowerCamelCase            =    AutoTokenizer.from_pretrained('''bert-base-cased'''							)
    __lowerCamelCase            =    {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
    __lowerCamelCase            =    load_dataset('''csv'''							,    data_files=UpperCamelCase__							)
    __lowerCamelCase            =    datasets['''train'''].unique('''label'''							)

    __lowerCamelCase            =    {v: i for i, v in enumerate(UpperCamelCase__							)}

    def tokenize_function(UpperCamelCase__							):
        # max_length=None => use the model max length (it's actually the default)
        __lowerCamelCase            =    tokenizer(
            examples['''sentence1''']							,    examples['''sentence2''']							,    truncation=UpperCamelCase__							,    max_length=UpperCamelCase__							,    padding='''max_length'''							)
        if "label" in examples:
            __lowerCamelCase            =    [label_to_id[l] for l in examples['''label''']]
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    __lowerCamelCase            =    datasets.map(
        UpperCamelCase__							,    batched=UpperCamelCase__							,    remove_columns=['''sentence1''', '''sentence2''', '''label''']							,    )

    def collate_fn(UpperCamelCase__							):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(UpperCamelCase__							,    padding='''max_length'''							,    max_length=1_28							,    return_tensors='''pt'''							)
        return tokenizer.pad(UpperCamelCase__							,    padding='''longest'''							,    return_tensors='''pt'''							)

    # Instantiate dataloaders.
    __lowerCamelCase            =    DataLoader(tokenized_datasets['''train''']							,    shuffle=UpperCamelCase__							,    collate_fn=UpperCamelCase__							,    batch_size=2							)
    __lowerCamelCase            =    DataLoader(tokenized_datasets['''validation''']							,    shuffle=UpperCamelCase__							,    collate_fn=UpperCamelCase__							,    batch_size=1							)

    return train_dataloader, eval_dataloader

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    __lowerCamelCase            =    0
    while len(UpperCamelCase__							) > 1:
        __lowerCamelCase            =    0
        # Consider two files with minimum cost to be merged
        for _ in range(2							):
            __lowerCamelCase            =    files.index(min(UpperCamelCase__							)							)
            temp += files[min_index]
            files.pop(UpperCamelCase__							)
        files.append(UpperCamelCase__							)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
  import doctest

  doctest.testmod()
